Achieving Intelligent Interactions with Analytical CRM

Increasingly competitive business and consumer marketplaces make it imperative for companies not only to attract customers, but also to retain them ­ especially that small percentage of highly profitable customers. Retention strategies for valued customers generally focus on financial and/or service-level incentives to promote loyalty. Because few companies enjoy the economies of scale (or investment capital) to sustain competitive differentiation on price alone, many businesses seek to maximize customer value by building loyalty through brand and service differentiation. This approach places a premium on the quality of every customer contact as each interaction serves to either build brand or destroy it.

Personalization offers great promise for improving customer interaction quality, but this promise can only be realized if intelligent interactions are enabled in real time across touchpoints. Real-time database marketing presents unique integration and automation challenges around data consolidation, customer analysis, treatment logic and operational integration. To achieve intelligent interactions, robust predictive modeling must be integrated with historical analysis and dynamic marketing logic across all customer data sources. The resulting customer insights and treatment strategies must then be delivered quickly and consistently across all touchpoints in the context of each customer interaction. Failure to consider the context of each customer interaction results in predictable personalization at best. Only by using real-time customer context information coupled with robust analytical CRM can you achieve predictive personalization and realize the desired business benefits.

Complete analytical CRM solutions incorporate data mining, business intelligence and campaign management applications to develop customer understanding and deliver appropriate customer treatments to operational CRM platforms. Operational CRM systems are designed for transactional efficiency around a particular customer touchpoint such as a Web site or call center. These operational systems address the sales and service automation aspects of CRM, whereas the marketing automation function is enabled by analytical CRM. The importance of flexible, yet powerful analytical CRM solutions is increasing dramatically as business needs dictate that organizations integrate all their touchpoint systems and deliver consistent, timely and relevant personalization across these channels.

Understand Business Drivers

Intelligent interactions are characterized by delivering added value to both the customer and the business. Measurable added-value objectives should be identified up front when implementing or refining a personalization strategy. While these metrics and their relative importance vary by industry and business model, suitable measurement methods and assessment criteria should be established and reviewed periodically. Just as test and control groups are used to build predictive models or refine marketing treatments, these same concepts should be extended to operational performance metrics to establish baselines from which to compare and assess results.

From a business perspective, analytical CRM can contribute to revenue enhancement and cost savings in many different ways. Examples include:

Improved shopper-to-buyer conversion rates through more relevant information and offers.

Increased purchased value through more targeted cross-selling and up-selling.

More profitable customers through improved customer retention and loyalty effects.

Enhanced service perception through relevant messaging during an interaction.

Improved service levels for best customers.

Analytical CRM infrastructure selection is challenging given the required solution components, organizational implications and system integration considerations. This is compounded by the multitude of point solutions, architecture differences and functional ambiguities in some vendors' solutions between what is achieved via software product features versus customization services. Selection criteria must go beyond simple return on investment (ROI) arguments to consider total cost of ownership (TCO) and time-to-business-value metrics. TCO can vary substantially depending on the ratio between software product functionality and customization services associated with initial deployment, subsequent enhancements and ongoing maintenance. Time-to-business-value reflects the need to quickly demonstrate business results within an organization to develop the necessary support for ongoing business and process improvements.Understanding the tradeoffs between application flexibility, ease of use and sophistication is vital. Analytical CRM solutions must be designed to quickly adapt to all the changing operational systems and data stores they inter-operate with to promote ongoing business value and mitigate the risk of early obsolescence. Some solution strategies attempt to first consolidate all necessary information into a single data mart to enable their particular application. This approach works fine for relatively static decision support applications; but it creates ETL-induced cycle time delays, can restrict real-time interaction capability and impedes the introduction of new marketing initiatives with different information requirements. Ideally, analytical CRM solutions will promote both early and ongoing success by effectively balancing these tradeoffs.

Analytical CRM Capabilities

Initial CRM implementations focused on improving the operational efficiencies of sales and service functions in areas such as outbound call centers, e-commerce systems and inbound service centers. Emphasis is now being placed on analytical CRM solutions that can apply proven database marketing methods and deliver intelligent interactions across these operational systems. This is being driven by the recognition that the customer understanding developed within marketing operations is increasingly a mission-critical function that must be centralized and used to drive CRM operations across marketing, sales and service to provide consistent personalization and branding.

Achieving intelligent interactions requires robust analytical methods coupled with sophisticated marketing automation and effective operational execution. Some of the most important analytical CRM capabilities are listed in Figure 1. The integrated application of these different analytical methods is required to maximize customer understanding and deliver intelligent interactions.

Figure 1: Analytical CRM Capabilities

Multisource data access and manipulation is an important technology that enables marketers to rapidly adapt to changing infrastructure and dynamic marketing campaign requirements. The flexibility to balance data movement and processing tradeoffs within an analytical CRM suite allows marketers to leverage their existing IT infrastructure to contain costs while retaining control over information assets and business goals.

Analytic processing capabilities require reporting, classification and forecasting. These capabilities are appropriate for different users and different purposes. Analytical processing methods support either predictive insight or historical data interpretation. Business intelligence reporting and online analytical processing (OLAP) are informative about past facts whereas data mining helps develop predictive understanding about future customer behaviors. Historical analysis is an important business tool for assessing what has happened. Effective utilization of predictive techniques is vital to maximize future business opportunities.

Campaign management is the engine behind marketing automation. It provides the application framework to implement business rules in the context of formal database marketing methodologies. Sophisticated campaign management applications also allow users to easily create and modify complex segmentation logic across many dimensions and at different levels of abstraction (e.g., household, customer or transaction). This capability quickly becomes important when marketers seek competitive advantage and desire intelligent customer interactions.

Personalization Considerations

Personalization methods have evolved quickly and will continue to do so. This evolution is being fueled by the growing importance of real-time customer interaction channels such as the Web and contact centers coupled with operational efficiencies realized from automating these channels and increasing customer expectations. Savvy online customers expect to realize benefits from online channel automation such as reduced service time, effective self service any time or personally relevant information. Marketers must not forget to balance the opportunity cost of each contact against the response likelihood and opt-out risk simply because online channel interaction costs are so cheap.

Historically, personalization methods have tended to focus on content assembly and delivery. Initial Web personalization was based on manual customer configuration of content. News services and portals personify this checkbox personalization, wherein a subscriber configures content by selecting interest categories.

More recently, Web operational platforms for content management and e-commerce have begun offering add-on or third-party products, allowing some limited automation of personalized content creation via hard-coded rules or recommendation engines. These approaches can be customer-profile driven. When they are profile- driven, the profiles should include predictive as well as historical attributes. Computation of predictive attributes is best vested in analytical CRM suites, given the substantive processing differences required. The operational CRM system can then maximize its rules engine capabilities as a fast, personalized content generator from externally supplied analytical attributes.

Today's personalization technologies range from ad hoc rule authoring to simplified predictive algorithms and hybrid combinations. Rule- based solutions offer marketers an initial intuitive appeal but have some practical limits due to coding complexity and maintainability as logic increases and inevitable rule conflicts occur. Predictive algorithms for cross- sell recommendations can offer some improvement over hard-coded rules, but their analysis approach and scope must be understood. Basing a personalized recommendation on a few page views or a one-time gift purchase can lead to unwelcome or out-of-date recommendations. Cross-sell recommendations can usually be improved by evaluating all pertinent online and off-line transaction and interaction data including stated or inferred preferences and relevant demographic data. Hybrid methods based on a combination of rules and predictive models can be built to address specific personalization scenarios and limitations of the individual methods. An important consideration for any hybrid solution is required staff expertise, maintainability and flexibility to meet the personalization scope and overall marketing objectives.

These current personalization technologies usually involve compromises between analytic depth/breadth, flexibility, speed and cross-channel data integration. There seems to be an inordinate emphasis on real-time modeling versus real-time scoring. Building predictive models on the fly requires substantial algorithmic compromises and often includes violations of statistical rigor. In most scenarios, robust predictive models built using established data mining algorithms against a richer input data set can be built offline and periodically refreshed. Real-time or batch customer scoring can then be used as appropriate. This approach allows different predictive attributes such as cross- sell offers, category affinities, customer value and response likelihood to be computed at appropriate times and refreshed at suitable intervals.